Stephen Chang, MD, PhD

All Publications

• An organism-wide atlas of hormonal signaling based on the mouse lemur single-cell transcriptome. Nature communications Liu, S., Ezran, C., Wang, M. F., Li, Z., Awayan, K., Long, J. Z., De Vlaminck, I., Wang, S., Epelbaum, J., Kuo, C. S., Terrien, J., Krasnow, M. A., Ferrell, J. E. 2024; 15 (1): 2188

  Abstract

  Hormones mediate long-range cell communication and play vital roles in physiology, metabolism, and health. Traditionally, endocrinologists have focused on one hormone or organ system at a time. Yet, hormone signaling by its very nature connects cells of different organs and involves crosstalk of different hormones. Here, we leverage the organism-wide single cell transcriptional atlas of a non-human primate, the mouse lemur (Microcebus murinus), to systematically map source and target cells for 84 classes of hormones. This work uncovers previously-uncharacterized sites of hormone regulation, and shows that the hormonal signaling network is densely connected, decentralized, and rich in feedback loops. Evolutionary comparisons of hormonal genes and their expression patterns show that mouse lemur better models human hormonal signaling than mouse, at both the genomic and transcriptomic levels, and reveal primate-specific rewiring of hormone-producing/target cells. This work complements the scale and resolution of classical endocrine studies and sheds light on primate hormone regulation.

  View details for DOI 10.1038/s41467-024-46070-9

  View details for PubMedID 38467625

  View details for PubMedCentralID 1540572

• FIRM: Flexible integration of single-cell RNA-sequencing data for large-scale multi-tissue cell atlas datasets. Briefings in bioinformatics Ming, J., Lin, Z., Zhao, J., Wan, X., Yang, C., Wu, A. R. 2022

  Abstract

  Single-cell RNA-sequencing (scRNA-seq) is being used extensively to measure the mRNA expression of individual cells from deconstructed tissues, organs and even entire organisms to generate cell atlas references, leading to discoveries of novel cell types and deeper insight into biological trajectories. These massive datasets are usually collected from many samples using different scRNA-seq technology platforms, including the popular SMART-Seq2 (SS2) and 10X platforms. Inherent heterogeneities between platforms, tissues and other batch effects make scRNA-seq data difficult to compare and integrate, especially in large-scale cell atlas efforts; yet, accurate integration is essential for gaining deeper insights into cell biology. We present FIRM, a re-scaling algorithm which accounts for the effects of cell type compositions, and achieve accurate integration of scRNA-seq datasets across multiple tissue types, platforms and experimental batches. Compared with existing state-of-the-art integration methods, FIRM provides accurate mixing of shared cell type identities and superior preservation of original structure without overcorrection, generating robust integrated datasets for downstream exploration and analysis. FIRM is also a facile way to transfer cell type labels and annotations from one dataset to another, making it a reliable and versatile tool for scRNA-seq analysis, especially for cell atlas data integration.

  View details for DOI 10.1093/bib/bbac167

  View details for PubMedID 35561293

• The Tabula Sapiens: A multiple-organ, single-cell transcriptomic atlas of humans. Science (New York, N.Y.) Jones, R. C., Karkanias, J., Krasnow, M. A., Pisco, A. O., Quake, S. R., Salzman, J., Yosef, N., Bulthaup, B., Brown, P., Harper, W., Hemenez, M., Ponnusamy, R., Salehi, A., Sanagavarapu, B. A., Spallino, E., Aaron, K. A., Concepcion, W., Gardner, J. M., Kelly, B., Neidlinger, N., Wang, Z., Crasta, S., Kolluru, S., Morri, M., Pisco, A. O., Tan, S. Y., Travaglini, K. J., Xu, C., Alcántara-Hernández, M., Almanzar, N., Antony, J., Beyersdorf, B., Burhan, D., Calcuttawala, K., Carter, M. M., Chan, C. K., Chang, C. A., Chang, S., Colville, A., Crasta, S., Culver, R. N., Cvijović, I., D'Amato, G., Ezran, C., Galdos, F. X., Gillich, A., Goodyer, W. R., Hang, Y., Hayashi, A., Houshdaran, S., Huang, X., Irwin, J. C., Jang, S., Juanico, J. V., Kershner, A. M., Kim, S., Kiss, B., Kolluru, S., Kong, W., Kumar, M. E., Kuo, A. H., Leylek, R., Li, B., Loeb, G. B., Lu, W. J., Mantri, S., Markovic, M., McAlpine, P. L., de Morree, A., Morri, M., Mrouj, K., Mukherjee, S., Muser, T., Neuhöfer, P., Nguyen, T. D., Perez, K., Phansalkar, R., Pisco, A. O., Puluca, N., Qi, Z., Rao, P., Raquer-McKay, H., Schaum, N., Scott, B., Seddighzadeh, B., Segal, J., Sen, S., Sikandar, S., Spencer, S. P., Steffes, L. C., Subramaniam, V. R., Swarup, A., Swift, M., Travaglini, K. J., Van Treuren, W., Trimm, E., Veizades, S., Vijayakumar, S., Vo, K. C., Vorperian, S. K., Wang, W., Weinstein, H. N., Winkler, J., Wu, T. T., Xie, J., Yung, A. R., Zhang, Y., Detweiler, A. M., Mekonen, H., Neff, N. F., Sit, R. V., Tan, M., Yan, J., Bean, G. R., Charu, V., Forgó, E., Martin, B. A., Ozawa, M. G., Silva, O., Tan, S. Y., Toland, A., Vemuri, V. N., Afik, S., Awayan, K., Botvinnik, O. B., Byrne, A., Chen, M., Dehghannasiri, R., Detweiler, A. M., Gayoso, A., Granados, A. A., Li, Q., Mahmoudabadi, G., McGeever, A., de Morree, A., Olivieri, J. E., Park, M., Pisco, A. O., Ravikumar, N., Salzman, J., Stanley, G., Swift, M., Tan, M., Tan, W., Tarashansky, A. J., Vanheusden, R., Vorperian, S. K., Wang, P., Wang, S., Xing, G., Xu, C., Yosef, N., Alcántara-Hernández, M., Antony, J., Chan, C. K., Chang, C. A., Colville, A., Crasta, S., Culver, R., Dethlefsen, L., Ezran, C., Gillich, A., Hang, Y., Ho, P. Y., Irwin, J. C., Jang, S., Kershner, A. M., Kong, W., Kumar, M. E., Kuo, A. H., Leylek, R., Liu, S., Loeb, G. B., Lu, W. J., Maltzman, J. S., Metzger, R. J., de Morree, A., Neuhöfer, P., Perez, K., Phansalkar, R., Qi, Z., Rao, P., Raquer-McKay, H., Sasagawa, K., Scott, B., Sinha, R., Song, H., Spencer, S. P., Swarup, A., Swift, M., Travaglini, K. J., Trimm, E., Veizades, S., Vijayakumar, S., Wang, B., Wang, W., Winkler, J., Xie, J., Yung, A. R., Artandi, S. E., Beachy, P. A., Clarke, M. F., Giudice, L. C., Huang, F. W., Huang, K. C., Idoyaga, J., Kim, S. K., Krasnow, M., Kuo, C. S., Nguyen, P., Quake, S. R., Rando, T. A., Red-Horse, K., Reiter, J., Relman, D. A., Sonnenburg, J. L., Wang, B., Wu, A., Wu, S. M., Wyss-Coray, T. 2022; 376 (6594): eabl4896

  Abstract

  Molecular characterization of cell types using single-cell transcriptome sequencing is revolutionizing cell biology and enabling new insights into the physiology of human organs. We created a human reference atlas comprising nearly 500,000 cells from 24 different tissues and organs, many from the same donor. This atlas enabled molecular characterization of more than 400 cell types, their distribution across tissues, and tissue-specific variation in gene expression. Using multiple tissues from a single donor enabled identification of the clonal distribution of T cells between tissues, identification of the tissue-specific mutation rate in B cells, and analysis of the cell cycle state and proliferative potential of shared cell types across tissues. Cell type-specific RNA splicing was discovered and analyzed across tissues within an individual.

  View details for DOI 10.1126/science.abl4896

  View details for PubMedID 35549404

• Publisher Correction: Cell types of origin of the cell-free transcriptome. Nature biotechnology Vorperian, S. K., Moufarrej, M. N., Tabula Sapiens Consortium, Quake, S. R., Jones, R. C., Karkanias, J., Krasnow, M., Pisco, A. O., Quake, S. R., Salzman, J., Yosef, N., Bulthaup, B., Brown, P., Harper, W., Hemenez, M., Ponnusamy, R., Salehi, A., Sanagavarapu, B. A., Spallino, E., Aaron, K. A., Concepcion, W., Gardner, J. M., Kelly, B., Neidlinger, N., Wang, Z., Crasta, S., Kolluru, S., Morri, M., Tan, S. Y., Travaglini, K. J., Xu, C., Alcantara-Hernandez, M., Almanzar, N., Antony, J., Beyersdorf, B., Burhan, D., Calcuttawala, K., Carter, M. M., Chan, C. K., Chang, C. A., Chang, S., Colville, A., Culver, R. N., Cvijovic, I., D'Amato, G., Ezran, C., Galdos, F. X., Gillich, A., Goodyer, W. R., Hang, Y., Hayashi, A., Houshdaran, S., Huang, X., Irwin, J. C., Jang, S., Juanico, J. V., Kershner, A. M., Kim, S., Kiss, B., Kong, W., Kumar, M. E., Kuo, A. H., Leylek, R., Li, B., Loeb, G. B., Lu, W., Mantri, S., Markovic, M., McAlpine, P. L., de Morree, A., Mrouj, K., Mukherjee, S., Muser, T., Neuhofer, P., Nguyen, T. D., Perez, K., Phansalkar, R., Puluca, N., Qi, Z., Rao, P., Raquer-McKay, H., Schaum, N., Scott, B., Seddighzadeh, B., Segal, J., Sen, S., Sikandar, S., Spencer, S. P., Steffes, L., Subramaniam, V. R., Swarup, A., Swift, M., Van Treuren, W., Trimm, E., Veizades, S., Vijayakumar, S., Vo, K. C., Vorperian, S. K., Wang, W., Weinstein, H. N., Winkler, J., Wu, T. T., Xie, J., Yung, A. R., Zhang, Y., Detweiler, A. M., Mekonen, H., Neff, N. F., Sit, R. V., Tan, M., Yan, J., Bean, G. R., Charu, V., Forgo, E., Martin, B. A., Ozawa, M. G., Silva, O., Toland, A., Vemuri, V. N., Afik, S., Awayan, K., Bierman, R., Botvinnik, O. B., Byrne, A., Chen, M., Dehghannasiri, R., Gayoso, A., Granados, A. A., Li, Q., Mahmoudabadi, G., McGeever, A., Olivieri, J. E., Park, M., Ravikumar, N., Stanley, G., Tan, W., Tarashansky, A. J., Vanheusden, R., Wang, P., Wang, S., Xing, G., Xu, C., Yosef, N., Culver, R., Dethlefsen, L., Ho, P., Liu, S., Maltzman, J. S., Metzger, R. J., Sasagawa, K., Sinha, R., Song, H., Wang, B., Artandi, S. E., Beachy, P. A., Clarke, M. F., Giudice, L. C., Huang, F. W., Huang, K. C., Idoyaga, J., Kim, S. K., Kuo, C. S., Nguyen, P., Rando, T. A., Red-Horse, K., Reiter, J., Relman, D. A., Sonnenburg, J. L., Wu, A., Wu, S. M., Wyss-Coray, T. 2022

  View details for DOI 10.1038/s41587-022-01293-3

  View details for PubMedID 35347330

• Cell types of origin of the cell-free transcriptome. Nature biotechnology Vorperian, S. K., Moufarrej, M. N., Tabula Sapiens Consortium, Quake, S. R., Jones, R. C., Karkanias, J., Krasnow, M., Pisco, A. O., Quake, S. R., Salzman, J., Yosef, N., Bulthaup, B., Brown, P., Harper, W., Hemenez, M., Ponnusamy, R., Salehi, A., Sanagavarapu, B. A., Spallino, E., Aaron, K. A., Concepcion, W., Gardner, J. M., Kelly, B., Neidlinger, N., Wang, Z., Crasta, S., Kolluru, S., Morri, M., Tan, S. Y., Travaglini, K. J., Xu, C., Alcantara-Hernandez, M., Almanzar, N., Antony, J., Beyersdorf, B., Burhan, D., Calcuttawala, K., Carter, M. M., Chan, C. K., Chang, C. A., Chang, S., Colville, A., Culver, R. N., Cvijovic, I., D'Amato, G., Ezran, C., Galdos, F. X., Gillich, A., Goodyer, W. R., Hang, Y., Hayashi, A., Houshdaran, S., Huang, X., Irwin, J. C., Jang, S., Juanico, J. V., Kershner, A. M., Kim, S., Kiss, B., Kong, W., Kumar, M. E., Kuo, A. H., Leylek, R., Li, B., Loeb, G. B., Lu, W., Mantri, S., Markovic, M., McAlpine, P. L., de Morree, A., Mrouj, K., Mukherjee, S., Muser, T., Neuhofer, P., Nguyen, T. D., Perez, K., Phansalkar, R., Puluca, N., Qi, Z., Rao, P., Raquer-McKay, H., Schaum, N., Scott, B., Seddighzadeh, B., Segal, J., Sen, S., Sikandar, S., Spencer, S. P., Steffes, L., Subramaniam, V. R., Swarup, A., Swift, M., Van Treuren, W., Trimm, E., Veizades, S., Vijayakumar, S., Vo, K. C., Vorperian, S. K., Wang, W., Weinstein, H. N., Winkler, J., Wu, T. T., Xie, J., Yung, A. R., Zhang, Y., Detweiler, A. M., Mekonen, H., Neff, N. F., Sit, R. V., Tan, M., Yan, J., Bean, G. R., Charu, V., Forgo, E., Martin, B. A., Ozawa, M. G., Silva, O., Toland, A., Vemuri, V. N., Afik, S., Awayan, K., Bierman, R., Botvinnik, O. B., Byrne, A., Chen, M., Dehghannasiri, R., Gayoso, A., Granados, A. A., Li, Q., Mahmoudabadi, G., McGeever, A., Olivieri, J. E., Park, M., Ravikumar, N., Stanley, G., Tan, W., Tarashansky, A. J., Vanheusden, R., Wang, P., Wang, S., Xing, G., Xu, C., Yosef, N., Culver, R., Dethlefsen, L., Ho, P., Liu, S., Maltzman, J. S., Metzger, R. J., Sasagawa, K., Sinha, R., Song, H., Wang, B., Artandi, S. E., Beachy, P. A., Clarke, M. F., Giudice, L. C., Huang, F. W., Huang, K. C., Idoyaga, J., Kim, S. K., Kuo, C. S., Nguyen, P., Rando, T. A., Red-Horse, K., Reiter, J., Relman, D. A., Sonnenburg, J. L., Wu, A., Wu, S. M., Wyss-Coray, T. 2022

  Abstract

  Cell-free RNA from liquid biopsies can be analyzed to determine disease tissue of origin. We extend this concept to identify cell types of origin using the Tabula Sapiens transcriptomic cell atlas as well as individual tissue transcriptomic cell atlases in combination with the Human Protein Atlas RNA consensus dataset. We define cell type signature scores, which allow the inference of cell types that contribute to cell-free RNA for a variety of diseases.

  View details for DOI 10.1038/s41587-021-01188-9

  View details for PubMedID 35132263

• RNA splicing programs define tissue compartments and cell types at single cell resolution. eLife Olivieri, J. E., Dehghannasiri, R., Wang, P. L., Jang, S., de Morree, A., Tan, S. Y., Ming, J., Ruohao Wu, A., Tabula Sapiens Consortium, Quake, S. R., Krasnow, M. A., Salzman, J. 2021; 10

  Abstract

  The extent splicing is regulated at single-cell resolution has remained controversial due to both available data and methods to interpret it. We apply the SpliZ, a new statistical approach, to detect cell-type-specific splicing in >110K cells from 12 human tissues. Using 10x data for discovery, 9.1% of genes with computable SpliZ scores are cell-type-specifically spliced, including ubiquitously expressed genes MYL6 and RPS24. These results are validated with RNA FISH, single-cell PCR, and Smart-seq2. SpliZ analysis reveals 170 genes with regulated splicing during human spermatogenesis, including examples conserved in mouse and mouse lemur. The SpliZ allows model-based identification of subpopulations indistinguishable based on gene expression, illustrated by subpopulation-specific splicing of classical monocytes involving an ultraconserved exon in SAT1. Together, this analysis of differential splicing across multiple organs establishes that splicing is regulated cell-type-specifically.

  View details for DOI 10.7554/eLife.70692

  View details for PubMedID 34515025

• A molecular cell atlas of the human lung from single-cell RNA sequencing. Nature Travaglini, K. J., Nabhan, A. N., Penland, L., Sinha, R., Gillich, A., Sit, R. V., Chang, S., Conley, S. D., Mori, Y., Seita, J., Berry, G. J., Shrager, J. B., Metzger, R. J., Kuo, C. S., Neff, N., Weissman, I. L., Quake, S. R., Krasnow, M. A. 2020

  Abstract

  Although single-cell RNA sequencing studies have begun to provide compendia of cell expression profiles1-9, it has been difficult to systematically identify and localize all molecularcell types in individual organs to create a full molecular cell atlas. Here, using droplet- and plate-based single-cell RNA sequencing of approximately 75,000 human cells across all lung tissue compartments and circulating blood, combined with a multi-pronged cell annotation approach, we create an extensive cell atlas of the human lung. We define the gene expression profiles and anatomical locations of 58 cell populations in the human lung, including 41 out of 45 previously known cell types and 14 previously unknown ones. This comprehensive molecular atlas identifies the biochemical functions of lung cells and the transcription factors and markers for making and monitoring them; defines the cell targets of circulating hormones and predicts local signalling interactions and immune cell homing; and identifies cell types that are directly affected by lung disease genes and respiratory viruses. By comparing human and mouse data, we identified 17 molecular cell types that have been gained or lost during lung evolution and others with substantially altered expression profiles, revealing extensive plasticity of cell types and cell-type-specific gene expression during organ evolution including expression switches between cell types. This atlas provides the molecular foundation for investigating how lung cell identities, functions and interactions are achieved in development and tissue engineering and altered in disease and evolution.

  View details for DOI 10.1038/s41586-020-2922-4

  View details for PubMedID 33208946

• Molecular profiling of dilated cardiomyopathy that progresses to heart failure. JCI insight Burke, M. A., Chang, S., Wakimoto, H., Gorham, J. M., Conner, D. A., Christodoulou, D. C., Parfenov, M. G., Depalma, S. R., Eminaga, S., Konno, T., Seidman, J. G., Seidman, C. E. 2016; 1 (6)

  Abstract

  Dilated cardiomyopathy (DCM) is defined by progressive functional and structural changes. We performed RNA-seq at different stages of disease to define molecular signaling in the progression from pre-DCM hearts to DCM and overt heart failure (HF) using a genetic model of DCM (phospholamban missense mutation, PLN(R9C/+)). Pre-DCM hearts were phenotypically normal yet displayed proliferation of nonmyocytes (59% relative increase vs. WT, P = 8 × 10(-4)) and activation of proinflammatory signaling with notable cardiomyocyte-specific induction of a subset of profibrotic cytokines including TGFβ2 and TGFβ3. These changes progressed through DCM and HF, resulting in substantial fibrosis (17.6% of left ventricle [LV] vs. WT, P = 6 × 10(-33)). Cardiomyocytes displayed a marked shift in metabolic gene transcription: downregulation of aerobic respiration and subsequent upregulation of glucose utilization, changes coincident with attenuated expression of PPARα and PPARγ coactivators -1α (PGC1α) and -1β, and increased expression of the metabolic regulator T-box transcription factor 15 (Tbx15). Comparing DCM transcriptional profiles with those in hypertrophic cardiomyopathy (HCM) revealed similar and distinct molecular mechanisms. Our data suggest that cardiomyocyte-specific cytokine expression, early fibroblast activation, and the shift in metabolic gene expression are hallmarks of cardiomyopathy progression. Notably, key components of these profibrotic and metabolic networks were disease specific and distinguish DCM from HCM.

  View details for PubMedID 27239561

  View details for PubMedCentralID PMC4882118

• 5 ' RNA-Seq identifies Fhl1 as a genetic modifier in cardiomyopathy JOURNAL OF CLINICAL INVESTIGATION Christodoulou, D. C., Wakimoto, H., Onoue, K., Eminaga, S., Gorham, J. M., Depalma, S. R., Herman, D. S., Teekakirikul, P., Conner, D. A., McKean, D. M., Domenighetti, A. A., Aboukhalil, A., Chang, S., Srivastava, G., McDonough, B., De Jager, P. L., Chen, J., Bulyk, M. L., Muehlschlege, J. D., Seidman, C. E., Seidman, J. G. 2014; 124 (3): 1364-1370

  Abstract

  The transcriptome is subject to multiple changes during pathogenesis, including the use of alternate 5' start-sites that can affect transcription levels and output. Current RNA sequencing techniques can assess mRNA levels, but do not robustly detect changes in 5' start-site use. Here, we developed a transcriptome sequencing strategy that detects genome-wide changes in start-site usage (5'RNA-Seq) and applied this methodology to identify regulatory events that occur in hypertrophic cardiomyopathy (HCM). Compared with transcripts from WT mice, 92 genes had altered start-site usage in a mouse model of HCM, including four-and-a-half LIM domains protein 1 (Fhl1). HCM-induced altered transcriptional regulation of Fhl1 resulted in robust myocyte expression of a distinct protein isoform, a response that was conserved in humans with genetic or acquired cardiomyopathies. Genetic ablation of Fhl1 in HCM mice was deleterious, which suggests that Fhl1 transcriptional changes provide salutary effects on stressed myocytes in this disease. Because Fhl1 is a chromosome X-encoded gene, stress-induced changes in its transcription may contribute to gender differences in the clinical severity of HCM. Our findings indicate that 5'RNA-Seq has the potential to identify genome-wide changes in 5' start-site usage that are associated with pathogenic phenotypes.

  View details for DOI 10.1172/JCI70108

  View details for Web of Science ID 000332347700050

  View details for PubMedID 24509080

  View details for PubMedCentralID PMC3934171

• Connexin43 Modulates Cell Polarity and Directional Cell Migration by Regulating Microtubule Dynamics PLOS ONE Francis, R., Xu, X., Park, H., Wei, C., Chang, S., Chatterjee, B., Lo, C. 2011; 6 (10)

  Abstract

  Knockout mice deficient in the gap junction gene connexin43 exhibit developmental anomalies associated with abnormal neural crest, primordial germ cell, and proepicardial cell migration. These migration defects are due to a loss of directional cell movement, and are associated with abnormal actin stress fiber organization and a loss of polarized cell morphology. To elucidate the mechanism by which Cx43 regulates cell polarity, we used a wound closure assays with mouse embryonic fibroblasts (MEFs) to examine polarized cell morphology and directional cell movement. Studies using embryonic fibroblasts from Cx43 knockout (Cx43KO) mice showed Cx43 deficiency caused cell polarity defects as characterized by a failure of the Golgi apparatus and the microtubule organizing center to reorient with the direction of wound closure. Actin stress fibers at the wound edge also failed to appropriately align, and stabilized microtubule (Glu-tubulin) levels were markedly reduced. Forced expression of Cx43 with deletion of its tubulin-binding domain (Cx43dT) in both wildtype MEFs and neural crest cell explants recapitulated the cell migration defects seen in Cx43KO cells. However, forced expression of Cx43 with point mutation causing gap junction channel closure had no effect on cell motility. TIRF imaging revealed increased microtubule instability in Cx43KO cells, and microtubule targeting of membrane localized Cx43 was reduced with expression of Cx43dT construct in wildtype cells. Together, these findings suggest the essential role of Cx43 gap junctions in development is mediated by regulation of the tubulin cytoskeleton and cell polarity by Cx43 via a nonchannel function.

  View details for DOI 10.1371/journal.pone.0026379

  View details for Web of Science ID 000295981600056

  View details for PubMedID 22022608

  View details for PubMedCentralID PMC3194834

• Genetics of hypertrophic cardiomyopathy CURRENT OPINION IN CARDIOLOGY Konno, T., Chang, S., Seidman, J. G., Seidman, C. E. 2010; 25 (3): 205-209

  Abstract

  Hypertrophic cardiomyopathy (HCM), the most common inherited cardiac disorder, exhibits remarkable genetic and clinical heterogeneity. This manuscript reviews recent discoveries of disease-causing genes and their clinical consequences, and provides an overview of research that aims to elucidate how HCM ensues from a single-nucleotide mutation.The spectrum of genes that are mutated in HCM has expanded. In combination with newly developed sequencing technologies, there are now robust strategies for gene-based diagnosis in HCM. Understanding the molecular pathophysiology of HCM has emerged from the study of genetically engineered animal models of disease, and new data indicate important roles for altered intracellular Ca²⁺ regulation and oxidative stress. Pharmacologic strategies to normalize these processes show promise in attenuating HCM in experimental models.The current repertoire of HCM genes allows effective gene-based diagnosis, information that enables accurate assessment of disease risk in family members, and provides some insight into clinical course. From mechanistic insights gleaned from fundamental investigations of experimental HCM models, novel therapeutic targets that may provide new benefits for HCM patients have surfaced.

  View details for DOI 10.1097/HCO.0b013e3283375698

  View details for Web of Science ID 000276879500006

  View details for PubMedID 20124998

  View details for PubMedCentralID PMC2932754